基于物理和数据驱动模型组合的全天候陆地表面温度的重建
Xuepeng Zhang1,2, Peng Gou3, Fengjiao Zhang4
1Research Center of Big Data Technology, Nanhu Laboratory, Jiaxing, 314000, China. 996588861@QQ.com.
概括
这项研究通过结合物理模型和数据驱动模型开发了一种全天候陆地表面温度 (LST) 产品. 新方法克服了远程传感热红外图像中的云污染,提供连续的LST数据.
科学领域:
- 地球和环境科学 地球和环境科学
- 遥感 遥感 遥感 遥感
- 地质物理学 地质物理学
背景情况:
- 遥感热红外 (TIR) 图像对于陆地表面温度 (LST) 监测至关重要.
- 云层覆盖经常污染TIR图像,限制了时空空间LST数据的完整性.
- 现有的方法难以提供完整的,无云的LST数据集.
研究的目的:
- 开发一种全天候的陆地表面温度 (LST) 产品.
- 克服云污染卫星热红外 (TIR) 数据的局限性.
- 以改善空间和时间完整性生成MODIS类型的LST数据.
主要方法:
- 将天气研究和预报 (WRF) 物理模型与随机森林 (RF) 数据驱动模型结合起来.
- 使用WRF生成LST源数据和RF以提高多源遥感数据的准确性.
- 开发了一个数据驱动的辅助物理模型框架,以生产全天候LST.
主要成果:
- 生成了所有天气MODIS类的LST数据,分辨率为1公里.
- 实现了高空间连续性和LST分布的准确恢复,即使有大量的云层覆盖.
- 报告的高精度指标:MAE < 1-2 K,RMSE < 2 K,和 ρ > 0.9,总体MAE为 0.80 K,RMSE为 1.09 K.
结论:
- 集成的物理和数据驱动模型有效地重建了全天候LST.
- 该模型为卫星TIR图像中云端诱导的数据缺口提供了有价值的解决方案.
- 生成的全天候LST数据为各种应用提供了高准确性和完整性.
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